Detecting low-level information carrying signals in high-level noise is a challenging task. Various signal processing techniques exist for separating noise components from information carrying signals. Performance of the various signal processing techniques may depend on the characteristics of noise and the characteristics of the information carrying signal. As an example, statistical signal processing methods can incorporate noise statistics for modeling the noise source and removing the noise. Signal processing methods can project the noisy signal into multiple subspaces in an attempt to separate the noise components from the information carrying components. Principal Component Analysis is one such method which decomposes a noisy signal into multiple principal components. The information carrying components may be distributed to only a few principal components, depending on the projection. However, PCA is data dependent and the projection may not effectively separate the information carrying signal from the noise signal.
Accordingly a need exists for a method of robust signal detection that is data independent.